程式碼:
a = 1
lsta = list(a)
print(lsta,type(lsta),len(lsta))
輸出:
Traceback (most recent call last):
File “C:\Python\Radar_20221005\untitled1.py”, line 3, in <module>
lsta = list(a)
TypeError: ‘int’ object is not iterable
int無法取list()
如果取array()呢?
程式碼:
import numpy as np
a = 1
arya = np.array(a)
print(arya, type(arya), len(arya))
輸出:
Traceback (most recent call last):
File “C:\Python\Radar_20221005\untitled1.py”, line 7, in <module>
print(arya,type(arya),len(arya))
TypeError: len() of unsized object
int可以取array(),
只是長度0 (不要誤會長度1)
所以取len()時錯誤
empty list: []長度也是0
去掉len()

3(1+2)的左右邊沒有[ ] 包覆
type為ndarray (0-dimensional)
用法同int
numpy官網:

2D array的運算:

推薦hahow線上學習python: https://igrape.net/30afN


![Python: List[ pandas.Series ] 轉DataFrame技巧:正確理解row和column的關係,同 concat( List[ pandas.Series ], axis=1 ).T Python: List[ pandas.Series ] 轉DataFrame技巧:正確理解row和column的關係,同 concat( List[ pandas.Series ], axis=1 ).T](https://i1.wp.com/savingking.com.tw/wp-content/uploads/2025/04/20250422150133_0_1cfa94.png?quality=90&zoom=2&ssl=1&resize=350%2C233)


![Python如何做excel的樞紐分析? groupbyObj = df.groupby([‘A’, ‘B’]) ; groupbyObj.apply() 跟 groupbyObj.agg() 差異為何? result = groupbyObj .apply( function(df) -> Series ) ; result_agg = groupbyObj .agg( [‘mean’, ‘std’] ) ; aggfunc(Series) -> float Python如何做excel的樞紐分析? groupbyObj = df.groupby([‘A’, ‘B’]) ; groupbyObj.apply() 跟 groupbyObj.agg() 差異為何? result = groupbyObj .apply( function(df) -> Series ) ; result_agg = groupbyObj .agg( [‘mean’, ‘std’] ) ; aggfunc(Series) -> float](https://i2.wp.com/savingking.com.tw/wp-content/uploads/2023/03/20230327140158_46.png?quality=90&zoom=2&ssl=1&resize=350%2C233)

![Python: pandas.DataFrame 如何找出重複值並計算重複次數? counts = df[duplicates] .groupby([‘name’]) .size() .reset_index(name=’count’) Python: pandas.DataFrame 如何找出重複值並計算重複次數? counts = df[duplicates] .groupby([‘name’]) .size() .reset_index(name=’count’)](https://i1.wp.com/savingking.com.tw/wp-content/uploads/2023/03/20230316131103_65.png?quality=90&zoom=2&ssl=1&resize=350%2C233)


近期留言